Vehicle electrification in a developing country: Status and issue,from a well-to-wheel perspective

The increase of public attention, scientific research and political interest in environmental problems associated with transportation has provided the motivation for re-invention of electric vehicles. However the usage of grid-dependent EVs with a high-carbon electricity grid might produce more damage to the environment. This study aims to provide an environmental impact comparison of ICEVs, HEVs and EVs during their usage cycle, by modeling their energy consumption (electricity or fuel) and the supply chains of the supplied energy, (well-to-wheel) based on a life cycle assessment. The results show that running EVs with the existing mixed sources of electrical energy produce larger impacts on the environment 60% of the time; when compared to HEVs. When compared to ICEVs, EVs produce a larger environmental impact on 7 out of 15 environmental impact categories. Overall the environmental impacts of EVs are substantial based on the well-to-wheel analysis. It will continue to be so if no change is made to the methods of electricity generation in the near future. Given that the environmental profile of EVs is linked with the existing national electricity generation mix, the national electricity supply must be made cleaner before the electrification of the urban transport system.     

Electric vehicles’ energy consumption measurement and estimation

Use of electric vehicles (EVs) has been viewed by many as a way to significantly reduce oil dependence, operate vehicles more efficiently, and reduce carbon emissions. Due to the potential benefits of EVs, the federal and local governments have allocated considerable funding and taken a number of legislative and regulatory steps to promote EV deployment and adoption. With this momentum, it is not difficult to see that in the near future EVs could gain a significant market penetration, particularly in densely populated urban areas with systemic air quality problems. We will soon face one of the biggest challenges: how to improve efficiency for EV transportation system? This research takes the first step in tackling this challenge by addressing a fundamental issue, i.e. how to measure and estimate EVs’ energy consumption. In detail, this paper first presents a system which can collect in-use EV data and vehicle driving data. This system then has been installed in an EV conversion vehicle built in this research as a test vehicle. Approximately 5 months of EV data have been collected and these data have been used to analyze both EV performance and driver behaviors. The analysis shows that the EV is more efficient when driving on in-city routes than driving on freeway routes. Further investigation of this particular EV driver’s route choice behavior indicates that the EV user tries to balance the trade-off between travel time and energy consumption. Although more data are needed in order to generalize this finding, this observation could be important and might bring changes to the traffic assignment for future transportation system with a significant share of EVs. Additionally, this research analyzes the relationships among the EV’s power, the vehicle’s velocity, acceleration, and the roadway grade. Based on the analysis results, this paper further proposes an analytical EV power estimation model. The evaluation results using the test vehicle show that the proposed model can successfully estimate EV’s instantaneous power and trip energy consumption. Future research will focus on applying the proposed EV power estimation model to improve EVs’ energy efficiency.     

Consumer purchase intentions for electric vehicles: Is green more important than price and range?

In view of global warming and climate change, a transition from combustion to electric vehicles (EVs) can help to reduce greenhouse gas emissions and improve air quality. However, high acquisition costs and short driving ranges are considered to be main factors which impede the diffusion of EVs. Since electricity needs to be produced from renewable energy sources for EVs to be a true green alternative, the environmental performance of EVs is also presumed to be an important factor. This paper investigates the role of environmental performance compared to price value and range confidence regarding consumer purchase intentions for EVs. To develop our hypothesis, we interview 40 end-user subjects about their beliefs toward EVs. Then, we perform 167 test drives with a plug-in battery EV and conduct a survey with the participants to test the hypothesis. Results of a structural equation modeling support the hypothesis that the environmental performance of EVs is a stronger predictor of attitude and thus purchase intention than price value and range confidence.     

Testing electric vehicle demand in ‘hybrid households’ using a reflexive survey

The debate over electric vehicles (EVs) pivots largely on issues of market demand: will consumers purchase a vehicle that provides substantially less driving range, yet can be refueled at home, than an otherwise comparable gasoline vehicle? Also, what role do other unique attributes of EVs play in the purchase decision? Most previous studies find that limited driving range is a serious market barrier; many of those same studies ignore or under-value other novel attributes. To probe these future consumer decision processes deeply and robustly, we first devised and conducted detailed, interactive and experiment-oriented interviews. Then, incorporating what we learned, we designed an innovative mail survey and administered it to 454 multi-car households in California. The four-stage mail survey included a video of EV use and recharging and other informational material, completion of a 3-day trip diary and map of activity locations, and vehicle choice experiments. In addition to propulsion systems, respondents made choices of body styles, driving ranges, and other features. We formalized and tested what we call the hybrid household hypothesis: households who choose EVs will be purposefully diversifying their vehicle holdings to achieve the unique advantages of different propulsion systems. The hypothesis is supported, given the assumptions in our experimental design. In fact, a significantly larger number of EVs are chosen than the minimum number that would support our hypothesis. We find that purchases of battery-powered EVs by hybrid households would account for between 7 and 18% of annual light duty vehicle sales in California. EVs sold to fleets and other households would be in addition to those identified by this study.     

Greenhouse gas emissions associated with electric vehicle charging: The impact of electricity generation mix in a developing country

Since 2012, the government has been promoting the electric vehicles and the development of related infrastructure to encourage local automakers to explore into the alternatively powered vehicles. However, the benefits of grid-dependent EVs can only be harvested under the condition that their use is coupled with a low carbon electricity grid. Thus, it is an additional challenge for Malaysia's that are largely dependent on fossil fuels for electricity generation. The object of this paper is to perform a well-to-wheel life cycle assessment for calculating the greenhouse gas emissions attributable to the usage of ICEVs, HEVs and EVs in Malaysian scenario. These emission calculations will provide the best information for policymakers, researchers, and investors to make appropriate and effective decisions on policies, research and investments in future transport energy. The results show that running EVs with national grid will produce an average of 7% more GHG emissions than HEVs at the same distance. However, they will produce an average of 19% less GHG emissions than the ICEVs. Overall the GHG emissions produced through the usage of EVs are substantial based on the well-to-wheel analysis, as the environmental profile of EVs is linked with the national grid. Therefore, in order to harvest the benefit of EVs towards climate change and global warming mitigation, massive modernization and transformation should be taken for the development of the national grid towards greener sources.     

Forecasting electricity demand of electric vehicles by analyzing consumers’ charging patterns

The spread of electric vehicles (EVs) and their increasing demand for electricity has placed a greater burden on electricity generation and the power grid. In particular, the problem of whether to expand the electricity power stations and distribution facilities due to the construction of EV charging stations is emerging as an immediate issue. To effectively meet the demand for additional electricity while ensuring the stability of the power grid, there is a need to accurately predict the charging demands for EVs. Therefore, this study estimates the changes in electricity charging demand based on consumer preferences for EVs, charging time of day, and types of electric vehicle supply equipment (EVSE) and elucidates the matters to be considered for constructing EV infrastructure. The results show that consumers mainly preferred charging during the evening. However, when we considered different types of EVSEs (public and private) in the analysis, people preferred to charge at public EVSEs during the day. During peak load time, people tended to prefer charging using fast public EVSEs, which shows that consumers considered the tradeoffs between the full charge time and the price for charging. Based on these findings, this study provides key political implications for policy makers to consider in taking preemptive measures to adjust the electricity supply infrastructure.     

Advances in consumer electric vehicle adoption research: A review and research agenda

In spite of the purported positive environmental consequences of electrifying the light duty vehicle fleet, the number of electric vehicles (EVs) in use is still insignificant. One reason for the modest adoption figures is that the mass acceptance of EVs to a large extent is reliant on consumers’ perception of EVs. This paper presents a comprehensive overview of the drivers for and barriers against consumer adoption of plug-in EVs, as well as an overview of the theoretical perspectives that have been utilized for understanding consumer intentions and adoption behavior towards EVs. In addition, we identify gaps and limitations in existing research and suggest areas in which future research would be able to contribute.     

Carbon and energy footprints of electric delivery trucks: A hybrid multi-regional input-output life cycle assessment

Due to frequent stop-and-go operation and long idling periods when driving in congested urban areas, the electrification of commercial delivery trucks has become an interesting topic nationwide. In this study, environmental impacts of various alternative delivery trucks including battery electric, diesel, diesel-electric hybrid, and compressed natural gas trucks are analyzed. A novel life cycle assessment method, an environmentally-extended multi-region input-output analysis, is utilized to calculate energy and carbon footprints throughout the supply chain of alternative delivery trucks. The uncertainties due to fuel consumption or other key parameter variations in real life, data ranges are taken into consideration using a Monte Carlo simulation. Furthermore, variations in regional electricity mix greenhouse gas emission are also considered to present a region-specific assessment for each vehicle type. According to the analysis results, although the battery electric delivery trucks have zero tailpipe emission, electric trucks are not expected to have lower environmental impacts compared to other alternatives. On average, the electric trucks have slightly more greenhouse emissions and energy consumption than those of other trucks. The regional analysis also indicates that the percentage of cleaner power sources in the electricity mix plays an important role in the life cycle greenhouse gas emission impacts of electric trucks.     

Consumer preferences for electric vehicles in lower tier cities of China: Evidences from south Jiangsu region

China is the world biggest market of electric vehicles (EVs) in terms of production and sales. Existing studies on consumer preferences for EVs in China have generally focused on first-tier cities, while little attention has been paid to the lower tier cities. This exploratory study investigates consumer preferences for EVs in lower tier cities of China, by collecting stated preference (SP) data in two second-tier cities and three third-tier cities in the south Jiangsu region of China. The discrete choice modeling analysis shows that Chinese consumers in lower-tier cities are generally sensitive to monetary attributes, charging service and driving range of EVs. They also perceive Chinese vehicle brands to be disadvantaged compared with European brands. When comparing the differences in second-tier versus third-tier cities, we find that consumers in third-tier cities are more sensitive to purchase price, subsidy of purchase, and coverage of charging stations than their second-tier counterparts. This study also highlights the role of different psychological effects, such as symbols of car ownership, normative-face influence, and risk aversion, in shaping consumer preferences for EVs in lower-tier cities of China. Our results provide important implications for contextualizing government policies and marketing strategies in line with the different sizes and characteristics of the cities in China.     

Dynamic operations and pricing of electric unmanned aerial vehicle systems and power networks

The emergence of electric unmanned aerial vehicle (E-UAV) technologies, albeit somewhat futuristic, is anticipated to pose similar challenges to the system operation as those of electric vehicles (EVs). Notably, the charging of EVs en-route at charging stations has been recognized as a significant type of flexible load for power systems, which often imposes non-negligible impacts on the power system operator’s decisions on electricity prices. Meanwhile, the charging cost based on charging time and price is part of the trip cost for the users, which can affect the spatio-temporal assignment of E-UAV traffic to charging stations. This paper aims at investigating joint operations of coupled power and electric aviation transportation systems that are associated with en-route charging of E-UAVs in a centrally controlled and yet dynamic setting, i.e., with time-varying travel demand and power system base load. Dynamic E-UAV charging assignment is used as a tool to smooth the power system load. A joint pricing scheme is proposed and a cost minimization problem is formulated to achieve system optimality for such coupled systems. Numerical experiments are performed to test the proposed pricing scheme and demonstrate the benefits of the framework for joint operations.     

How much should gasoline be taxed when electric vehicles conquer the market? An analysis of the mismatch between efficient and existing gasoline taxes under emerging electric mobility

The taxation of gasoline is characterized by large variability across countries and recent research has analyzed existing gasoline tax levels from an economic efficiency point of view focusing on conventional internal combustion engine vehicles. Most studies find that existing fuel tax rates do not coincide with economically efficient levels. As long as policymakers do not take action to reduce the resulting efficiency gap, there will be an ongoing welfare loss to the economy. However, the composition of passenger car fleets will probably be subject to fundamental changes in the (near) future due to the emergence of electric mobility. This raises the question of whether the mismatch between current and efficient fuel taxation will persist, shrink, or even exacerbate under emerging electric mobility. This paper aims at answering this question by determining the structure and level of optimal gasoline taxes in the presence of electric vehicles (EVs). First, the optimal (nationwide) gasoline tax is analytically derived employing a general equilibrium approach. It is shown that differences in traffic related marginal external costs among fuel powered cars and EVs affect the corrective Pigouvian component of the optimal gasoline tax while a differential tax treatment influences the fiscal rational of the tax. Second, the model is applied to Germany using differentiated data on e.g. external costs and behavioral responses. Under a wide range of scenarios, the present analyses indicate a strong relationship between optimal gasoline taxes and electric mobility, calling for a downward adjustment of efficient gasoline taxes. The effect is mainly driven by financial incentives for purchasing and using EVs. Since fuel is likely to be undertaxed in many countries, the emergence of electric mobility will therefore close the gap between gasoline taxes in place and economically efficient taxes. On the other side, it will increase the efficiency gap in those countries where gasoline is overtaxed. This also has important implications for policy concerned with environmental objectives. Pushing electric mobility seriously and at the same time taxing gasoline efficiently could actually prevent sufficient CO₂ emission savings. However, at least in the case of Germany, even a downward adjusted optimal gasoline tax under electric mobility is likely to be higher than the current (non-optimal) tax.     

Modeling the effects of congestion on fuel economy for advanced power train vehicles

Fuel-speed curves (FSC) are used to account for the aggregate effects of congestion on fuel consumption in transportation scenario analysis. This paper presents plausible FSC for conventional internal combustion engine (ICE) vehicles and for advanced vehicles such as hybrid electric vehicles, fully electric vehicles (EVs), and fuel cell vehicles (FCVs) using a fuel consumption model with transient driving schedules and a set of 145 hypothetical vehicles. The FSC shapes show that advanced power train vehicles are expected to maintain fuel economy (FE) in congestion better than ICE vehicles, and FE can even improve for EV and FCV in freeway congestion. In order to implement these FSC for long-range scenario modeling, a bounded approach is presented which uses a single congestion sensitivity parameter. The results in this paper will assist analysis of the roles that vehicle technology and congestion mitigation can play in reducing fuel consumption and greenhouse gas emissions from motor vehicles.     

Electric and hydrogen buses: Shifting from conventionally fuelled cars in the UK

For the UK to meet their national target of net zero emissions as part of the central Paris Agreement target, further emphasis needs to be placed on decarbonizing public transport and moving away from personal transport (conventionally fuelled vehicles (CFVs) and electric vehicles (EVs)). Electric buses (EBs) and hydrogen buses (HBs) have the potential to fulfil requirements if powered from low carbon renewable energy sources.A comparison of carbon dioxide (CO₂) emissions produced from conventionally fuelled buses (CFB), EBs and HBs between 2017 and 2050 under four National Grid electricity scenarios was conducted. In addition, emissions per person at different vehicle capacity levels (100%, 75%, 50% and 25%) were projected for CFBs, HBs, EBs and personal transport assuming a maximum of 80 passengers per bus and four per personal vehicle.Results indicated that CFVs produced 30 gCO₂ km⁻¹ per person compared to 16.3 gCO₂ km⁻¹ per person by CFBs by 2050. At 100% capacity, under the two-degree scenario, CFB emissions were 36 times higher than EBs, 9 times higher than HBs and 12 times higher than EVs in 2050. Cumulative emissions under all electricity scenarios remained lower for EBs and HBs.Policy makers need to focus on encouraging a modal shift from personal transport towards sustainable public transport, primarily EBs as the lowest level emitting vehicle type. Simple electrification of personal vehicles will not meet the required targets. Simultaneously, CFBs need to be replaced with EBs and HBs if the UK is going to meet emission targets.     

Numerical analysis of electric bus fast charging strategies for demand charge reduction

Electric transit buses have been recognized as an important alternative to diesel buses with many environmental benefits. Electric buses employing lithium titanate batteries can provide uninterrupted transit service thanks to their ability of fast charging. However, fast charging may result in high demand charges which will increase the fuel costs thereby limiting the electric bus market penetration. In this paper, we simulated daily charging patterns and demand charges of a fleet of electric buses in Tallahassee, Florida and identified an optimal charging strategy to minimize demand charges. It was found that by using a charging threshold of 60–64%, a $160,848 total saving in electricity cost can be achieved for a five electric bus fleet, comparing to a charging threshold of 0–28%. In addition, the impact of fleet sizes on the fuel cost was investigated. Fleets of 4 and 12 buses will achieve the lowest cost per mile driven when one fast charger is installed.     

The role of instrumental,hedonic and symbolic attributes in the intention to adopt electric vehicles

The aim is to understand how private car drivers’ perception of vehicle attributes may affect their intention to adopt electric vehicles (EVs). Data are obtained from a national online survey of potential EV adopters in the UK. The results indicate that instrumental attributes are important largely because they are associated with other attributes derived from owning and using EVs, including pleasure of driving (hedonic attributes) and identity derived from owning and using EVs (symbolic attributes). People who believe that a pro-environmental self-identity fits with their self-image are more likely to have positive perceptions of EV attributes. Perceptions of EV attributes are only very weakly associated with car-authority identity.     

Simulation of electric vehicle driver behaviour in road transport and electric power networks

The integration of electric vehicles (EVs) will affect both electricity and transport systems and research is needed on finding possible ways to make a smooth transition to the electrification of the road transport. To fully understand the EV integration consequences, the behaviour of the EV drivers and its impact on these two systems should be studied. This paper describes an integrated simulation-based approach, modelling the EV and its interactions in both road transport and electric power systems. The main components of both systems have been considered, and the EV driver behaviour was modelled using a multi-agent simulation platform. Considering a fleet of 1000 EV agents, two behavioural profiles were studied (Unaware/Aware) to model EV driver behaviour. The two behavioural profiles represent the EV driver in different stages of EV adoption starting with Unaware EV drivers when the public acceptance of EVs is limited, and developing to Aware EV drivers as the electrification of road transport is promoted in an overall context. The EV agents were modelled to follow a realistic activity-based trip pattern, and the impact of EV driver behaviour was simulated on a road transport and electricity grid. It was found that the EV agents’ behaviour has direct and indirect impact on both the road transport network and the electricity grid, affecting the traffic of the roads, the stress of the distribution network and the utilization of the charging infrastructure.     

Shared autonomous electric vehicle (SAEV) operations across the Austin,Texas network with charging infrastructure decisions

Shared autonomous vehicles, or SAVs, have attracted significant public and private interest because of their opportunity to simplify vehicle access, avoid parking costs, reduce fleet size, and, ultimately, save many travelers time and money. One way to extend these benefits is through an electric vehicle (EV) fleet. EVs are especially suited for this heavy usage due to their lower energy costs and reduced maintenance needs. As the price of EV batteries continues to fall, charging facilities become more convenient, and renewable energy sources grow in market share, EVs will become more economically and environmentally competitive with conventionally fueled vehicles. EVs are limited by their distance range and charge times, so these are important factors when considering operations of a large, electric SAV (SAEV) fleet.This study simulated performance characteristics of SAEV fleets serving travelers across the Austin, Texas 6-county region. The simulation works in sync with the agent-based simulator MATSim, with SAEV modeling as a new mode. Charging stations are placed, as needed, to serve all trips requested (under 75 km or 47 miles in length) over 30 days of initial model runs. Simulation of distinctive fleet sizes requiring different charge times and exhibiting different ranges, suggests that the number of station locations depends almost wholly on vehicle range. Reducing charge times does lower fleet response times (to trip requests), but increasing fleet size improves response times the most. Increasing range above 175 km (109 miles) does not appear to improve response times for this region and trips originating in the urban core are served the quickest. Unoccupied travel accounted for 19.6% of SAEV mileage on average, with driving to charging stations accounting for 31.5% of this empty-vehicle mileage. This study found that there appears to be a limit on how much response time can be improved through decreasing charge times or increasing vehicle range.     

Electric vehicles’ energy consumption estimation with real driving condition data

The use of electric vehicles (EVs) is viewed as an attractive option to reduce CO₂ emissions and fuel consumption resulted from transport sector, but the popularization of EVs has been hindered by the cruising range limitation and the charging process inconvenience. Energy consumption characteristics analysis is the important foundation to study charging infrastructures locating, eco-driving behavior and energy saving route planning, which are helpful to extend EVs’ cruising range. From a physical and statistical view, this paper aims to develop a systematic energy consumption estimation approach suitable for EV actual driving cycles. First, by employing the real second-by-second driving condition data collected on typical urban travel routes, the energy consumption characteristics analysis is carried out specific to the microscopic driving parameters (instantaneous speed and acceleration) and battery state of charge (SOC). Then, based on comprehensive consideration of the mechanical dynamics characteristics and electric machine system of the EVs, a set of energy consumption rate estimation models are established under different operation modes from a statistical perspective. Finally, the performance of proposed model is fully evaluated by comparing with a conventional energy consumption estimation method. The results show that the proposed modeling approach represents a significant accuracy improvement in the estimation of real-world energy consumption. Specifically, the model precision increases by 25.25% in decelerating mode compared to the conventional model, while slight improvement in accelerating and cruising mode with desirable goodness of fit.     

Smart charging management for shared autonomous electric vehicle fleets: A Puget Sound case study

Increasingly, experts are forecasting the future of transportation to be shared, autonomous and electric. As shared autonomous electric vehicle (SAEV) fleets roll out to the market, the electricity consumed by the fleet will have significant impacts on energy demand and, in turn, drive variation in energy cost and reliability, especially if the charging is unmanaged. This research proposes a smart charging (SC) framework to identify benefits of active SAEV charging management that strategically shifts electricity demand away from high-priced peak hours or towards renewable generation periods. Time of use (TOU), real time pricing (RTP), and solar generation electricity scenarios are tested using an agent-based simulation to study (1) the impact of battery capacity and charging infrastructure type on SAEV fleet performance and operational costs under SC management; (2) the cost reduction potential of SC considering energy price fluctuation, uncertainty, and seasonal variation; (3) the charging infrastructure requirements; and (4) the system efficiency of powering SAEVs with solar generation. A case study from the Puget Sound region demonstrates the proposed SC algorithm using trip patterns from the regional travel demand model and local energy prices. Results suggest that in the absence of electricity price signals, SAEV charging demand is likely to peak the evening, when regional electricity use patterns already indicate high demand. Under SC management, EVs with larger battery sizes are more responsive to low-electricity cost charging opportunities, and have greater potential to reduce total energy related costs (electricity plus charging infrastructure) for a SAEV fleet, especially under RTP structure.